A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity

Authors:
Chunming Li;Rui Huang;Zhaohua Ding;Chris Gatenby;Dimitris Metaxas;John Gore
Affiliations:
Vanderbilt University Institute of Imaging Science, USA;Department of Computer Science, Rutgers University, USA;Vanderbilt University Institute of Imaging Science, USA;Vanderbilt University Institute of Imaging Science, USA;Department of Computer Science, Rutgers University, USA;Vanderbilt University Institute of Imaging Science, USA
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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Abstract

This paper presents a variational level set approach to joint segmentation and bias correction of images with intensity inhomogeneity. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the intensity inhomogeneity. We first define a weighted K-means clustering objective function for image intensities in a neighborhood around each point, with the cluster centers having a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain and incorporated into a variational level set formulation. The energy minimization is performed via a level set evolution process. Our method is able to estimate bias of quite general profiles. Moreover, it is robust to initialization, and therefore allows automatic applications. The proposed method has been used for images of various modalities with promising results.